Predictive generation of search suggestions

ABSTRACT

A method of generating search suggestions includes receiving an indication of a current location of the user device. After a user accesses a search function on the user device and before the user submits a search request, the method includes determining search results associated with locations in proximity to the current location of the user device, using query log data indicating selections of past search results by users after presentation of the past search results in response to respective past queries. The method also includes determining relative positioning of the search results based on the number of users that selected each search result, determining categories associated with the search results, grouping the search results by the determined categories; and sending the grouped search results and the one or more categories associated with the search results to the user device for display according to the determined relative positioning.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 15/201,617, filed Jul. 4, 2016, which is a continuation of U.S.patent application Ser. No. 13/158,796, filed Jun. 13, 2011, thedisclosures of which are incorporated herein by reference in itsentirety for all purposes.

TECHNICAL FIELD

This specification relates to providing information relevant to usersearch queries.

BACKGROUND

Internet search engines identify resources, e.g., Web pages, images,text documents, and multimedia content, in response to queries submittedby users and present information about the resources in a manner that isuseful to the users.

Users of search engines are often interested in information specific totheir location. For example, users may want to know the local weather,or may be interested in local politics or sports. Some topics which areinteresting to users of one location are less interesting to users in adifferent location.

A search engine allows a user to provide an input for which searchresults are returned in response. Some search engines can providesuggestions to the user based upon the search query the user entered.For example, some search engines provide search query suggestions basedupon the current search query.

SUMMARY

This patent application describes systems and techniques for sendingmessages, such as short message service (SMS) messages and voicemessages, from a computing device.

In general, one innovative aspect of the subject matter described inthis specification can be embodied in methods that include the actionsof receiving a first location from a user device. The actions includeobtaining a user profile associated with a user of the user device. Theactions include determining, prior to receiving a search request, searchresults associated with locations in proximity to the first locationusing the user profile. The actions also include sending the searchresults to the user device.

Other embodiments of this aspect include corresponding computer systems,apparatus, and computer programs recorded on one or more computerstorage devices, each configured to perform the actions of the methods.A system of one or more computers can be configured to performparticular actions by virtue of having software, firmware, hardware, ora combination of them installed on the system that in operation causesor cause the system to perform the actions. One or more computerprograms can be configured to perform particular actions by virtue ofincluding instructions that, when executed by data processing apparatus,cause the apparatus to perform the actions.

The foregoing and other embodiments can each optionally include one ormore of the following features, alone or in combination. Determining aplurality of search results associated with locations in proximity tothe first location may include using at least one of user profile data,user social graph data, or query log data together with the firstlocation to determine the plurality of search results. Each searchresult may reference a document, the document being associated with asecond location in proximity to the first location. The actions mayinclude determining a query suggestion based on the first location. Theactions may include sending the query suggestion to the user device.Determining the query suggestion based on the first location may includeusing at least one of user profile data, social graph data or query logdata together with the first location to determine the query suggestion.Determining query suggestions based on the first location may includeidentifying queries submitted by prior users in proximity to the firstlocation. Determining query suggestions based on the first location mayinclude obtaining a user profile associated with a user of the userdevice. Determining query suggestions based on the first location mayinclude using information obtained from the user profile to identifyqueries submitted by prior users near the first location. Determiningquery suggestions may include identifying queries previously submittedby the user. Determining query suggestions may include determining oneor more categories associated with the search results. Determining querysuggestions may include sending the one or more categories associatedwith the search results to the user device. The actions may includedetermining one or more categories associated with the search results.The actions may include sending the one or more categories associatedwith the search results to the user device. Sending the search resultsmay include sending the search results grouped by category. The actionsmay include receiving a partial query from the user device. The actionsmay include filtering the search results based on the partial queryprior to sending the search results to the user device. The action mayinclude determining a search category associated with at least one ofthe search results. The actions may include grouping the search resultsassociated with the search category. The action may include providingthe search category together with the search results associated with thesearch category to the user.

All or part of the systems and techniques described herein may beimplemented as a computer program product that includes instructionsthat are stored on one or more non-transitory machine-readable storagemedia, and that are executable on one or more processing devices.Examples of non-transitory machine-readable storage media include e.g.,read-only memory, an optical disk drive, memory disk drive, randomaccess memory, or the like. All or part of the systems and techniquesdescribed herein may be implemented as an apparatus, method, orelectronic system that may include one or more processing devices andmemory to store executable instructions to implement the statedfunctions.

The details of one or more implementations are set forth in theaccompanying drawings and the description below. Other features,objects, and advantages will be apparent from the description anddrawings, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 illustrates an example of a user accessing local suggestionsusing a mobile device.

FIG. 2 illustrates an example of a system for presentation of predictivesearch results.

FIG. 3A-C illustrates examples of mobile devices provided withpredictive search results.

FIG. 4 is a flow chart of an example of a process for generatingpredictive search results.

FIG. 5 is a block diagram of computing devices that may be used toimplement the systems and methods described in this document, as eithera client or as a server or plurality of servers.

DETAILED DESCRIPTION

FIG. 1 illustrates an example of a user accessing local suggestionsusing a mobile device. In this example, a user 102 is located in an areathat includes a cinema 104, a restaurant 106, and a theater 108. Theuser 102 accesses a search function on a mobile device 110. The searchfunction includes a query input field 112. Before the user 102 enters aquery into the query input field 112, the mobile device 110, working inconjunction with a search system (not shown), displays search results,query suggestions, and categories associated with the locations near thelocation of the user 102. For example, the mobile device 110 displays asearch result 116 for the cinema 104. A location may be considered nearthe user if it is within a predefined distance from the mobile device(for example, within 1 mile, 2 miles, or 10 miles).

The search results displayed on the mobile device 110 can vary dependingon the interests of the user 102. For example, the mobile device 110 maynot display a search result corresponding to the theater 108 based on adetermination that the user 102 is unlikely to be interested in such aresult. For situations in which the systems discussed here collectinformation about users, the users may be provided with an opportunityto opt in/out of programs or features that may collect information(e.g., information about a user's preferences, a user's contributions tosocial content providers, user's devices, etc.).

The mobile device 110 can also display search results based on thehistoric popularity of previously provided search results at locationsnear the location of the mobile device. For example, if a significantnumber of prior users of the search system have selected search resultsassociated with the restaurant 106, the mobile device may present thesearch result 114 on the mobile device 110. In some implementations, therelative positioning of the search results on the mobile device 110 canbe determined, at least in part, based on the popularity of prior searchresults selected by prior users.

The mobile device can display suggested search queries based on thelocation of the mobile device and user profile information. For example,if a user has historically searched for “Grocery,” the mobile device maydisplay a query suggestion “Grocery.” Query suggestions can also bebased on historic data collected from other users. For example, if alarge number of prior users have searched for “Laundromat” fromlocations near the mobile device, the mobile device may present“Laundromat” as a query suggestion.

In some implementations, the mobile device can group search results andquery suggestions according to categories. For example, the mobiledevice 110 can display a restaurant category 118 or a coffee shopcategory 120. In some implementations, the search results determined tobe most likely of interest to the user 102 are presented on the initialsearch screen and other search results determined to be of less interestto the user 102 are placed into categories.

FIG. 2 illustrates an example of a system for presentation of predictivesearch results. The process shown in FIG. 1 may be implemented in thesystem. In FIG. 2, mobile device 202 sends messages to a search system206 over a network 204, for example, the Internet. The mobile device 202can connect to the network through a variety of methods, for example,over a cellular network, a wireless network, or another radio network.The search system 206 sends search results, query suggestions andcategories to the mobile device 202. The search system 206 may performthese activities before, or while the user enters input into a query boxon the mobile device 202.

In some implementations, the mobile device 202 sends an indication ofthe location of the mobile device 202 to the search system 206. Forexample, the mobile device 202 can send global positioning system (GPS)coordinates, an IP address or another indication of the location of themobile device 202. The mobile device 202 can further send an indicationof the identity of the user of the mobile device 202. For example, themobile device 202 may send a user identifier, user name, e-mail address,or a session identifier that enables the search system to associate theuser of the mobile device 202 with a user profile. The mobile device 202can also send a partial query, such as the first few characters enteredby the user (e.g. “Ic”) entered by the user.

In some implementations, the mobile device 202 can send each characterentered by the user as part of a partial query to the search system 206as it is entered. In other implementations, the mobile device 202 storescharacters entered by the user until a minimum number of characters arestored (for example, 3) and then the mobile device 202 sends the storedcharacters in a partial query to the search system 206. In otherimplementations, the mobile device 202 waits until the user pauses whenentering characters and then sends the entered characters to the searchsystem 206. For example, the mobile device may wait until the user hasstopped entering text for one second before sending the partial queryincluding all of the entered characters to the search system 206.

The search system 206 includes an input component 208 which receives themessages from the mobile device 202. The input component 208 can verifythat the message from the mobile device 202 does not indicate that theuser of the mobile device 202 has submitted a search request. Verifyingthat the user has not submitted a search request may include verifyingthat the user has not used the mechanism in the user interface (e.g.,pressing a search button) or any other mechanism (e.g., a voicecommand), to send input to the search engine indicating that the userhas finished entering input. The input component 208 can also considerother factors, for example, whether the user has entered a partial queryhaving a minimum number of characters, or whether a predetermined amountof time has passed since the user entered the last character of thepartial query.

The input component 208 sends the message to a predictive resultscomponent 210. The predictive results component 210 obtains querysuggestions and/or search results based on information provided by themobile device 202. Query suggestions and/or search results can begenerated using query logs 214, location data 218, user profile data212, and social graph data 216.

Query logs 214 may include a history of search activity of prior users.The query logs 214 can include previously submitted search queries byother users and by the current user. The query logs 214 can also includeindications of which search results provided in response to thepreviously submitted queries were selected by the other users or by thecurrent user. Query logs 214 can be used to identify previouslysubmitted search queries and search results that are associated withlocations near the location of the mobile device 202. Previouslysubmitted queries can be identified by examining the query logs forqueries submitted by prior users from locations near the location of themobile device 202. Previously submitted search results can be identifiedby examining prior query logs to identify search results which werepresented in response to queries submitted by prior users from locationsnear to the location of the mobile device 202 and which weresubsequently selected by the prior users. The previously submittedqueries and previously submitted search results can be evaluated todetermine if they should be presented to the user of the mobile deviceas predictive queries and predictive search results. In someimplementations, evaluating the previously submitted queries and searchresults includes assigning a quality score to each previously submittedquery and search result, as is discussed below.

In some implementations, each prior user session can be associated witha location. In other implementations, each previously submitted queryand selected search result can be associated with a location.

The previously submitted queries can be provided with a query logquality score based on the popularity of the query at the location atwhich the query was submitted. Query suggestions may be provided a scorebased on the popularity of the search query in a given location. Querysuggestions may also be provided a score based on the quality of searchresults generated from the query. For example, a query suggestion thatpreviously produced search results that were subsequently selected maybe considered superior to a query suggestion that did not produce anyselected search results.

In some implementations, the query log quality score for the previouslysubmitted queries and search results can be calculated at the time themessage is received by the input processor 206 from the mobile device202. For example, the query log quality score can be based on the numberof times the query was submitted or the search result was selectedwithin a predefined distance of the mobile device (for example, 2miles).

In other implementations, the query log quality score for a previouslysubmitted query or a selected search result can be determined prior toreceiving a message from the mobile device. For example, differentlocations can be grouped together into regions (for example, by zipcode, city or town). At regular intervals (e.g. hourly, daily, weekly,etc.), the query log quality score for a previously submitted query or aselected search result can be calculated based on the number of timesthe query was submitted within the region over a period of time (e.g. aweek, a month, etc.).

In some implementations, queries previously submitted by the user of themobile device 202 can be used as query suggestions. Similarly, searchresults selected by the user of the mobile device 202 can be used assearch results.

Search results can be identified and scored based on location data 218.Location data 218 can include information associating resources withlocation. For example, a web page for a restaurant can be associatedwith the location of the restaurant. The predictive results component210 can identify resources associated with locations near the locationof the mobile device 202 as search results in which the user of themobile device 202 may be interested. Each search result can beassociated with a location quality score based on the distance betweenthe location associated with the resource and the location of the mobiledevice 202.

Search results and query suggestions can be further refined using userprofile data 212. For example, a profile of a user may includeinformation that the user is interested in cinema but not interested intheater. Consequently, query suggestions and search results related tocinema may be promoted, while suggestions and results related to theatermay be demoted or eliminated.

User profile data 212 can include information explicitly shared by theuser. For example, the user may provide a list of hobbies and interests.The user profile data 212 can also include information implicitlyidentified about the user. For example, a user's previously submittedqueries, selected search results, subscribed news feeds, and othercollected information can identify that a user is interested in aparticular topic. In general, the user may be provided with anopportunity to opt in/out of programs or features that may collect suchinformation about the user (e.g., information about a user'spreferences, a user's contributions to social content providers, user'sdevices, etc.). Moreover, such information about the user may beanonomized before being stored as user profile data 212.

User profile information can also include a history of other activity inwhich the user has engaged. For example, if the user has searched for aparticular address using a mapping function associated with the mobiledevice, the user profile may store the recently searched address.Similarly, the predictive results component 210 may identify queriesthat the user has previously submitted or search results that the userhas previously selected. These previously submitted queries and selectedresults may be presented as query suggestions and search results.

Search results and query suggestions can be further refined using socialgraph data 216. Generally, a social graph reflects social relationshipsbetween different users. For example, two users may have identified eachother as acquaintances, friends, or colleagues. The relationship may becaptured by a social graph. Social graph data 216 can be used toindicate that a user may be more or less interested in a search resultor query suggestion. For example, if a member of the user's social graphmakes available information indicating that he is currently at aparticular restaurant, the predictive results component may determinethat the user is more likely to be interested in a resource referencingthat restaurant. A user is also more likely to be interested in topicsthat are of interest to other members in the user's social graph.Similarly, if a member of the user's social graph highly rates arestaurant, film, or other activity or experience, the predictiveresults component may determine that the user is more likely to beinterested in a resource referencing that restaurant, film, activity orexperience.

In some implementations, the search system can provide a list ofcategories, which are likely to be of interest to the user. Thecategories can be identified based on the user profile data 212, thequery logs 214, the social graph data 216 and the location data 218. Forexample, if a user is located in Wrigleyville, Ill., the user may bepresented with a baseball category. The user's profile may also provideinformation about categories, for example, if the user has an interestin movies, a cinema category may be presented. Categories can also beidentified from search results and predictive query terms. In someimplementations, categories are derived from keywords associated withresources that are associated with locations near the mobile device.

FIGS. 3A-C illustrate examples of mobile devices provided withpredictive search results. Referring to FIG. 3A, a user accesses asearch function on a mobile device 302. Before the user enters anyinformation into the query input field 304, the user interface of themobile device is populated with predictive query suggestions and searchresults based, at least in part, on the location of the mobile device302. For example, the user interface may include a predictive searchresult for the international airport 306, an art supplies store 308, ora recently searched for address 310.

The user interface may also include query suggestions. For example, theuser interface displays a query suggestion “Laundromats” 312.

The user interface may also include grouping of information the user maybe interested in. For example, the “my places” 314 category may identifylocations previously identified by the user. The recent items categorygroup 316 may include search queries and search results recentlyobtained by the user. The starred places category 318 identifies searchresults that have been previously tagged by the user of the mobiledevice 302 and are located near the mobile device 302.

The user interface also includes a categories group 320. In someimplementations, the categories group provides access to differentcategories that include predictive search results grouped by the contentof the resource referenced by the predictive search result. In someimplementations, the user interface can also display category groupsdirectly on the user interface. For example, the mobile device 302displays a coffee shops category 322 and a restaurants category 324.

In some implementations, the presence of an arrow 370 indicates that theitem contains nested entries.

Referring to FIG. 3B, the mobile device 302 displays the restaurantcategory 326 on the user interface. The user interface includespredictive search results and subcategories. For example, the mobiledevice 302 displays search results for “oyster house” 328 a, “townmarket” 328 b, “carriage house” 328 c, and “crab shack” 328 d. The userinterface also displays subcategories, for example, an “American”sub-category 330 a, a “barbecue” sub-category 330 b, a “breakfast”sub¬category 330 c, a “Chinese” sub-category 330 d, and a “fast food”sub-category 330 e.

In some implementations, a down arrow 332 indicates that the user canscroll to display more sub-categories.

Referring to FIG. 3C, the user has entered a partial query “Ic” 332 intothe query input field 304. In response, the mobile device 302 sends thepartial query to a search service that can provide predictive queries,categories, and search results that include the partial query. Forexample, the mobile device 302 displays predictive search results “MooIce Cream Shop” 334, “Icapital LLC” 342, and “ICM” 334. The mobiledevice 302 also displays predictive suggested queries “Ice Cream Shop”336, “Ice Skating Rink” 338, and “Ice Supplier” 342.

FIG. 4 is a flow chart of an example of a process 400 for generatingpredictive search results. The process may be performed, for example, bythe predictive results generator 210 shown in FIG. 2. For convenience,the process will be described in relation to a system performing theprocess.

The process receives 402 a first location of a user device. The userdevice can be, for example, a mobile device such as a portable computer,cellular phone, smart phone, tablet computer, or other such device. Thelocation can be information that can be used to identify the location ofthe user device, such as GPS coordinates. In some implementations, anInternet Protocol address can be sufficient to identify a location ofthe user device. Similarly, cellular tower information associated withthe user device can be used to identify the location of

the user device. The location can be received, for example, as an HTTPrequest message sent from the user device.

The process obtains 404 a user profile associated with a user of theuser device. The user can be identified by a user identifier, a cookie,or a session identifier provided by the user device. The user profilecan reveal, for example, a description of the interests of the user(e.g., hobbies, search history, etc.). The user profile can alsoidentify social graphs of the user. In some implementations, the socialgraph may be centered on the user (e.g. the user identifies hisassociates), in other implementations, the user may have identifiedsocial networks in which the user is a member.

The user profile may also include information the user has implicitlyrevealed. For example, if a user frequently searches for model trains,the user profile may include information indicating the user has aninterest in model trains. In some implementation, the user may beprovided the opportunity to opt in or opt out of such data collectionand storage activities.

The process determines 406 search results associated with locations inproximity to the first location. The search results can be based on theuser profile as discussed above with respect to FIG. 2. For example, ifa user's profile indicates that the user is interested in model trainsthen resources associated with model trains and located in proximity tothe user may be determined.

The process sends 408 the search results to the user device. The searchresults can be sent to the user device, for example, as an HTTP responsemessage.

FIG. 5 is a block diagram of computing devices 500, 550 that may be usedto implement the systems and methods described in this document, aseither a client or as a server or plurality of servers. Computing device500 is intended to represent various forms of digital computers, such aslaptops, desktops, workstations, personal digital assistants, servers,blade servers, mainframes, and other appropriate computers. Computingdevice 550 is intended to represent various forms of mobile devices,such as personal digital assistants, cellular telephones, smartphones,and other similar computing devices. Additionally computing device 500or 550 can include Universal Serial Bus (USB) flash drives. The USBflash drives may store operating systems and other applications. The USBflash drives can include input/output components, such as a wirelesstransmitter or USB connector that may be inserted into a USB port ofanother computing device. The components shown here, their connectionsand relationships, and their functions, are meant to be exemplary only,and are not meant to limit implementations of the inventions describedand/or claimed in this document.

Computing device 500 includes a processor 502, memory 504, a storagedevice 506, a high-speed interface 508 connecting to memory 504 andhighspeed expansion ports 510, and a low speed interface 512 connectingto low speed bus 514 and storage device 506. Each of the components 502,504, 506, 508, 510, and 512, are interconnected using various busses,and may be mounted on a common motherboard or in other manners asappropriate. The processor 502 can process instructions for executionwithin the computing device 500, including instructions stored in thememory 504 or on the storage device 506 to display graphical informationfor a GUI on an external input/output device, such as display coupled tohigh speed interface 508. In other implementations, multiple processorsand/or multiple buses may be used, as appropriate, along with multiplememories and types of memory. Also, multiple computing devices 500 maybe connected, with each device providing portions of the necessaryoperations (e.g., as a server bank, a group of blade servers, or amulti-processor system).

The memory 504 stores information within the computing device 500. Inone implementation, the memory 504 is a volatile memory unit or units.In another implementation, the memory 504 is a non-volatile memory unitor units. The memory 504 may also be another form of computer-readablemedium, such as a magnetic or optical disk.

The storage device 506 is capable of providing mass storage for thecomputing device 500. In one implementation, the storage device 506 maybe or contain a computer-readable medium, such as a floppy disk device,a hard disk device, an optical disk device, or a tape device, a flashmemory or other similar solid state memory device, or an array ofdevices, including devices in a storage area network or otherconfigurations. The computer program product may also containinstructions that, when executed, perform one or more methods, such asthose described above. The information carrier is a computer- ormachine-readable medium, such as the memory 504, the storage device 506,or memory on processor 502.

The high speed controller 508 manages bandwidth-intensive operations forthe computing device 500, while the low speed controller 512 manageslower bandwidth-intensive operations. Such allocation of functions isexemplary only. In one implementation, the high-speed controller 508 iscoupled to memory 504, display 516 (e.g., through a graphics processoror accelerator), and to high-speed expansion ports 510, which may acceptvarious expansion cards (not shown). In the implementation, low-speedcontroller 512 is coupled to storage device 506 and low-speed expansionport 514. The low-speed expansion port, which may include variouscommunication ports (e.g., USB, Bluetooth, Ethernet, wireless Ethernet)may be coupled to one or more input/output devices, such as a keyboard,a pointing device, a scanner, or a networking device such as a switch orrouter, e.g., through a network adapter.

The computing device 500 may be implemented in a number of differentforms, as shown in the figure. For example, it may be implemented as astandard server 520, or multiple times in a group of such servers. Itmay also be implemented as part of a rack server system 524. Inaddition, it may be implemented in a personal computer such as a laptopcomputer 522. Alternatively, components from computing device 500 may becombined with other components in a mobile device (not shown), such asdevice 550. Each of such devices may contain one or more of computingdevice 500, 550, and an entire system may be made up of multiplecomputing devices 500, 550 communicating with each other.

Computing device 550 includes a processor 552, memory 564, aninput/output device such as a display 554, a communication interface566, and a transceiver 568, among other components. The device 550 mayalso be provided with a storage device, such as a microdrive or otherdevice, to provide additional storage. Each of the components 550, 552,564, 554, 566, and 568, are interconnected using various buses, andseveral of the components may be mounted on a common motherboard or inother manners as appropriate.

The processor 552 can execute instructions within the computing device550, including instructions stored in the memory 564. The processor maybe implemented as a chipset of chips that include separate and multipleanalog and digital processors. Additionally, the processor may beimplemented using any of a number of architectures. For example, theprocessor 410 may be a CISC (Complex Instruction Set Computers)processor, a RISC (Reduced Instruction Set Computer) processor, or aMISC (Minimal Instruction Set Computer) processor. The processor mayprovide, for example, for coordination of the other components of thedevice 550, such as control of user interfaces, applications run bydevice 550, and wireless communication by device 550.

Processor 552 may communicate with a user through control interface 558and display interface 556 coupled to a display 554. The display 554 maybe, for example, a TFT (Thin-Film-Transistor Liquid Crystal Display)display or an OLED (Organic Light Emitting Diode) display, or otherappropriate display technology. The display interface 556 may compriseappropriate circuitry for driving the display 554 to present graphicaland other information to a user. The control interface 558 may receivecommands from a user and convert them for submission to the processor552. In addition, an external interface 562 may be provide incommunication with processor 552, so as to enable near areacommunication of device 550 with other devices. External interface 562may provide, for example, for wired communication in someimplementations, or for wireless communication in other implementations,and multiple interfaces may also be used.

The memory 564 stores information within the computing device 550. Thememory 564 can be implemented as one or more of a computer-readablemedium or media, a volatile memory unit or units, or a non-volatilememory unit or units. Expansion memory 574 may also be provided andconnected to device 550 through expansion interface 572, which mayinclude, for example, a SIMM (Single In Line Memory Module) cardinterface. Such expansion memory 574 may provide extra storage space fordevice 550, or may also store applications or other information fordevice 550. Specifically, expansion memory 574 may include instructionsto carry out or supplement the processes described above, and mayinclude secure information also. Thus, for example, expansion memory 574may be provide as a security module for device 550, and may beprogrammed with instructions that permit secure use of device 550. Inaddition, secure applications may be provided via the SIMM cards, alongwith additional information, such as placing identifying information onthe SIMM card in a non-hackable manner.

The memory may include, for example, flash memory and/or NVRAM memory,as discussed below. The computer program product contains instructionsthat, when executed, perform one or more methods, such as thosedescribed above. The information carrier is a computer- ormachine-readable medium, such as the memory 564, expansion memory 574,or memory on processor 552 that may be received, for example, overtransceiver 568 or external interface 562.

Device 550 may communicate wirelessly through communication interface566, which may include digital signal processing circuitry wherenecessary. Communication interface 566 may provide for communicationsunder various modes or protocols, such as GSM voice calls, SMS, EMS, orMMS messaging, CDMA, TDMA, PDC, WCDMA, CDMA2000, or GPRS, among others.Such communication may occur, for example, through radio-frequencytransceiver 568. In addition, short-range communication may occur, suchas using a Bluetooth, WiFi, or other such transceiver (not shown). Inaddition, GPS (Global Positioning System) receiver module 570 mayprovide additional navigation- and location-related wireless data todevice 550, which may be used as appropriate by applications running ondevice 550.

Device 550 may also communicate audibly using audio codec 560, which mayreceive spoken information from a user and convert it to usable digitalinformation. Audio codec 560 may likewise generate audible sound for auser, such as through a speaker, e.g., in a handset of device 550. Suchsound may include sound from voice telephone calls, may include recordedsound (e.g., voice messages, music files, etc.) and may also includesound generated by applications operating on device 550.

The computing device 550 may be implemented in a number of differentforms, as shown in the figure. For example, it may be implemented as acellular telephone 580. It may also be implemented as part of asmartphone 582, personal digital assistant, or other similar mobiledevice.

Various implementations of the systems and techniques described here canbe realized in digital electronic circuitry, integrated circuitry,specially designed ASICs (application specific integrated circuits),computer hardware, firmware, software, and/or combinations thereof.These various implementations can include implementation in one or morecomputer programs that are executable and/or interpretable on aprogrammable system including at least one programmable processor, whichmay be special or general purpose, coupled to receive data andinstructions from, and to transmit data and instructions to, a storagesystem, at least one input device, and at least one output device.

These computer programs (also known as programs, software, softwareapplications or code) include machine instructions for a programmableprocessor, and can be implemented in a high-level procedural and/orobject-oriented programming language, and/or in assembly/machinelanguage. As used herein, the terms “machine-readable medium”“computer-readable medium” refers to any computer program product,apparatus and/or device (e.g., magnetic discs, optical disks, memory,Programmable Logic Devices (PLDs)) used to provide machine instructionsand/or data to a programmable processor, including a machine-readablemedium that receives machine instructions as a machine-readable signal.The term “machine-readable signal” refers to any signal used to providemachine instructions and/or data to a programmable processor.

To provide for interaction with a user, the systems and techniquesdescribed here can be implemented on a computer having a display device(e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor)for displaying information to the user and a keyboard and a pointingdevice (e.g., a mouse or a trackball) by which the user can provideinput to the computer. Other kinds of devices can be used to provide forinteraction with a user as well; for example, feedback provided to theuser can be any form of sensory feedback (e.g., visual feedback,auditory feedback, or tactile feedback); and input from the user can bereceived in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in acomputing system that includes a back end component (e.g., as a dataserver), or that includes a middleware component (e.g., an applicationserver), or that includes a front end component (e.g., a client computerhaving a graphical user interface or a Web browser through which a usercan interact with an implementation of the systems and techniquesdescribed here), or any combination of such back end, middleware, orfront end components. The components of the system can be interconnectedby any form or medium of digital data communication (e.g., acommunication network). Examples of communication networks include alocal area network (“LAN”), a wide area network (“WAN”), peer-to-peernetworks (having ad-hoc or static members), grid computinginfrastructures, and the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

A number of embodiments have been described. Nevertheless, it will beunderstood that various modifications may be made without departing fromthe spirit and scope of the invention. For example, various forms of theflows shown above may be used, with steps re-ordered, added, or removed.Also, although several applications of generating predictive results andmethods have been described, it should be recognized that numerous otherapplications are contemplated. Accordingly, other embodiments are withinthe scope of the following claims.

What is claimed is:
 1. A method of generating search suggestions, themethod comprising: receiving, by one or more processors from a userdevice, an indication of a current location of the user device; after auser accesses a search function on the user device and before the usersubmits a search request, determining search results associated withlocations in proximity to the current location of the user device, usingquery log data indicating selections of past search results by usersafter presentation of the past search results in response to respectivepast queries, by the one or more processors, wherein determining searchresults associated with locations in proximity to the current locationof the user device further comprises using at least one of user profiledata or user social graph data together with the current location of theuser device to determine the search results; determining, by the one ormore processors, relative positioning of the search results based on thenumber of two or more users that selected each search result;determining, by the one or more processors, categories associated withthe search results; grouping, by the one or more processors, the searchresults by the determined categories; determining one or moresubcategories of the categories associated with the search results; andsending the grouped search results, the one or more categories, and theone or more subcategories associated with the search results to the userdevice for display according to the determined relative positioning. 2.The method of claim 1, wherein each search result references a document,the document being associated with a second location in proximity to thecurrent location of the user device.
 3. The method of claim 1, furthercomprising: determining a query suggestion based on the current locationof the user device; and sending the query suggestion to the user device.4. The method of claim 3, wherein determining the query suggestion basedon the current location of the user device further comprises using atleast one of user profile data, social graph data or query log datatogether with the current location of the user device to determine thequery suggestion.
 5. The method of claim 4, wherein determining querysuggestions based on the current location of the user device comprises:identifying queries submitted by prior users in proximity to the currentlocation of the user device.
 6. The method of claim 3, whereindetermining query suggestions based on the current location comprisesfurther comprises: obtaining a user profile associated with a user ofthe user device; and using information obtained from the user profile toidentify queries submitted by prior users near the current location ofthe user device.
 7. The method of claim 3, wherein determining querysuggestions comprises: identifying queries previously submitted by theuser.
 8. The method of claim 1, wherein sending the grouped searchresults comprises sending the search results grouped by category.
 9. Themethod of claim 1, further comprising: determining a search categoryassociated with at least one of the search results; grouping the searchresults associated with the search category; and providing the searchcategory together with the search results associated with the searchcategory to the user.
 10. A system comprising: one or more computers andone or more storage devices storing instructions that are operable, whenexecuted by the one or more computers, to cause the one or morecomputers to perform operations comprising: receiving an indication of acurrent location of a user device, after a user accesses a searchfunction on the user device and before the user submits a searchrequest, determining search results associated with locations inproximity to the current location of the user device, using query logdata indicating selections of past search results by users afterpresentation of the past search results in response to respective pastqueries, by the one or more processors, wherein determining searchresults associated with locations in proximity to the current locationof the user device further comprises using at least one of user profiledata or user social graph data together with the current location of theuser device to determine the search results, determining relativepositioning of the search results based on the number of two or moreusers that selected each search result, determining categoriesassociated with the search results, grouping, by the one or moreprocessors, the search results by the determined categories, determiningone or more subcategories of the categories associated with the searchresults; and sending the grouped search results, the one or morecategories, and the one or more subcategories, associated with thesearch results to the user device for display according to thedetermined relative positioning.
 11. The system of claim 10, whereineach search result references a document, the document being associatedwith a second location in proximity to the current location of the userdevice.
 12. The system of claim 10, further comprising: determining aquery suggestion based on the current location of the user device; andsending the query suggestion to the user device.
 13. The system of claim12, wherein determining the query suggestion based on the currentlocation of the user device further comprises using at least one of userprofile data, social graph data or query log data together with thecurrent location of the user device to determine the query suggestion.14. The system of claim 13, wherein determining query suggestions basedon the current location of the user device comprises: identifyingqueries submitted by prior users in proximity to the current location ofthe user device.
 15. The system of claim 12, wherein determining querysuggestions based on the current location comprises further comprises:obtaining a user profile associated with a user of the user device; andusing information obtained from the user profile to identify queriessubmitted by prior users near the current location of the user device.16. The system of claim 12, wherein determining query suggestionscomprises: identifying queries previously submitted by the user.
 17. Amethod comprising: providing, on a user device, an interactive searchfunction with a search query input field; after a user accesses thesearch function on the user device, sending from the user device a firstlocation of the user device; receiving, at one or more processors, thefirst location from the user device; determining, by one or moreprocessors and prior to receiving a search request, search resultsassociated with locations in proximity to the first location of the userdevice, wherein the search results are determined using query log dataof previously submitted queries, wherein the query log data indicatesselections of search results by users after presentation of the searchresults; wherein determining search results associated with locations inproximity to the first location of the user device further comprisesusing at least one of user profile data or user social graph datatogether with the first location of the user device to determine thesearch results determining relative positioning of the search resultsbased on the number users that selected each search result; determiningcategories associated with the search results; grouping the searchresults by the determined categories; sending the grouped search resultsand the one or more categories associated with the search results to theuser device for display according to the determined relativepositioning; receiving, with the user device, the grouped searchresults; displaying, on the user device, the grouped search results inthe determined relative positioning under the query input field.